Production AI, engineered end to end, six eval-gated service lines.
The same playbook, tuned to the constraints of the sectors we ship into most.
Proof, not promises, selected case studies and recognition.
A transparent, 3-phase playbook from first audit to embedded team.
The senior team behind the work, and how to reach us.
AI that sells,
not just suggests.
In retail, AI earns its keep in conversion, basket size, and margin. We build personalisation, semantic search, and demand forecasting that lift revenue per visitor and take cost out of fulfilment and support, measured against your numbers, not a benchmark.
Six ways we deploy AI in e-commerce.
From semantic search to demand forecasting every engagement is scoped around your catalogue, your data, and the revenue levers that matter most.
Semantic Product Search
Vector search over your catalogue. Customers find what they mean, not just what they typed. Handles synonyms, misspellings, and intent.
Personalisation Engine
Real-time product recommendations based on browsing, purchase history, and contextual signals. Not collaborative filtering from 2012.
AI Customer Service
LLM-powered support agents trained on your FAQs, policies, and product data. Handle returns, tracking, and product questions without a human.
Demand & Inventory Forecasting
ML models that predict sell-through rates, seasonal demand, and stockout risk. Reduce overstock and lost sales simultaneously.
Dynamic Pricing
Pricing models that respond to competitor prices, demand signals, and inventory levels in real time. With guardrails to protect margin floors.
Review & Sentiment Intelligence
NLP pipelines over product reviews and support tickets. Surface quality issues, competitive intelligence, and NPS drivers automatically.
Four challenges we solve first.
Thousands of SKUs, attributes, and categories require ML, not if-else logic. We build for catalogue scale from day one not as a retrofit.
Returning 1,000 results is not helpful. AI search needs to understand intent and rank by business value, not just keyword match.
Users want relevant suggestions, not surveillance. We design personalisation with privacy-first data practices built into the architecture.
New SKUs have no history. Content-based embeddings and category priors handle cold-start without waiting for data to accumulate.
Twenty-plus AI systems shipped to production. One playbook, six industries, and a team that stays past launch.
Before you brief us.
A semantic search upgrade typically takes 4–6 weeks: data audit, embedding model selection, index build, and A/B test setup. Most clients see measurable conversion improvement within the first test cycle.
Yes. We've built AI layers on top of Shopify, Magento, WooCommerce, and custom platforms. We expose AI features via API so your storefront doesn't need a full rebuild.
At that scale we typically use a two-stage retrieval pipeline: fast approximate nearest-neighbour search to retrieve candidates, followed by a reranking model that applies business rules and personalisation signals.
Order status, returns and refunds, product questions, and policy lookups the queries that make up 70–80% of ticket volume for most retailers. Escalations route to human agents with full context.
Let's talk about
what you're building.
30 minutes, one of our seniors, no slide deck. By the end of the call you'll know whether we're the right team, and if not, who is.